Summary of Conformal Risk Control For Ordinal Classification, by Yunpeng Xu et al.
Conformal Risk Control for Ordinal Classification
by Yunpeng Xu, Wenge Guo, Zhi Wei
First submitted to arxiv on: 1 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The authors develop conformal risk control methods for ordinal classification tasks, which have numerous real-world applications. Building upon existing conformal prediction approaches, they formulate the ordinal classification problem within this framework, providing theoretical risk bounds. Two novel loss functions are introduced, designed specifically for ordinal classification, and corresponding algorithms are developed to determine prediction sets that control risks at a desired level. The effectiveness of these methods is demonstrated on three datasets, including simulated and real-world examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates new ways to predict outcomes in situations where there’s an order or ranking involved, like how good or bad something is. They use existing ideas to make this work better and create special formulas for this type of problem. The results are shown on three different datasets, including some fake data and real-world examples. |
Keywords
» Artificial intelligence » Classification
